import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD


torch.set_grad_enabled(False)





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_=False       )		->	Union[str, Any]:







 '''simple docstring'''
 UpperCamelCase			  =      []
 for i in range(config.num_hidden_layers       ):
  # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
  rename_keys.append((f"""module.blocks.{i}.norm1.weight""", f"""vit.encoder.layer.{i}.layernorm_before.weight""")       )
  rename_keys.append((f"""module.blocks.{i}.norm1.bias""", f"""vit.encoder.layer.{i}.layernorm_before.bias""")       )
  rename_keys.append(
      (f"""module.blocks.{i}.attn.proj.weight""", f"""vit.encoder.layer.{i}.attention.output.dense.weight""")       )
  rename_keys.append((f"""module.blocks.{i}.attn.proj.bias""", f"""vit.encoder.layer.{i}.attention.output.dense.bias""")       )
  rename_keys.append((f"""module.blocks.{i}.norm2.weight""", f"""vit.encoder.layer.{i}.layernorm_after.weight""")       )
  rename_keys.append((f"""module.blocks.{i}.norm2.bias""", f"""vit.encoder.layer.{i}.layernorm_after.bias""")       )
  rename_keys.append((f"""module.blocks.{i}.mlp.fc1.weight""", f"""vit.encoder.layer.{i}.intermediate.dense.weight""")       )
  rename_keys.append((f"""module.blocks.{i}.mlp.fc1.bias""", f"""vit.encoder.layer.{i}.intermediate.dense.bias""")       )
  rename_keys.append((f"""module.blocks.{i}.mlp.fc2.weight""", f"""vit.encoder.layer.{i}.output.dense.weight""")       )
  rename_keys.append((f"""module.blocks.{i}.mlp.fc2.bias""", f"""vit.encoder.layer.{i}.output.dense.bias""")       )

 # projection layer + position embeddings
 rename_keys.extend(
     [
         ("""module.cls_token""", """vit.embeddings.cls_token"""),
         ("""module.patch_embed.proj.weight""", """vit.embeddings.patch_embeddings.projection.weight"""),
         ("""module.patch_embed.proj.bias""", """vit.embeddings.patch_embeddings.projection.bias"""),
         ("""module.pos_embed""", """vit.embeddings.position_embeddings"""),
     ]       )

 if base_model:
  # layernorm + pooler
  rename_keys.extend(
      [
          ("""module.norm.weight""", """layernorm.weight"""),
          ("""module.norm.bias""", """layernorm.bias"""),
      ]       )

  # if just the base model, we should remove "vit" from all keys that start with "vit"
  UpperCamelCase			  =      [(pair[0], pair[1][4:]) if pair[1].startswith("""vit"""       ) else pair for pair in rename_keys]
 else:
  # layernorm + classification head
  rename_keys.extend(
      [
          ("""norm.weight""", """vit.layernorm.weight"""),
          ("""norm.bias""", """vit.layernorm.bias"""),
          ("""head.weight""", """classifier.weight"""),
          ("""head.bias""", """classifier.bias"""),
      ]       )

 return rename_keys





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_=False       )		->	Optional[int]:







 '''simple docstring'''
 for i in range(config.num_hidden_layers       ):
  if base_model:
   UpperCamelCase			  =      """"""
  else:
   UpperCamelCase			  =      """vit."""
  # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
  UpperCamelCase			  =      state_dict.pop(f"""module.blocks.{i}.attn.qkv.weight"""       )
  UpperCamelCase			  =      state_dict.pop(f"""module.blocks.{i}.attn.qkv.bias"""       )
  # next, add query, keys and values (in that order) to the state dict
  UpperCamelCase			  =      in_proj_weight[
      : config.hidden_size, :
  ]
  UpperCamelCase			  =      in_proj_bias[: config.hidden_size]
  UpperCamelCase			  =      in_proj_weight[
      config.hidden_size : config.hidden_size * 2, :
  ]
  UpperCamelCase			  =      in_proj_bias[
      config.hidden_size : config.hidden_size * 2
  ]
  UpperCamelCase			  =      in_proj_weight[
      -config.hidden_size :, :
  ]
  UpperCamelCase			  =      in_proj_bias[-config.hidden_size :]





def 			lowercase(	UpperCamelCase_       )		->	Optional[int]:







 '''simple docstring'''
 UpperCamelCase			  =      ["""head.weight""", """head.bias"""]
 for k in ignore_keys:
  state_dict.pop(UpperCamelCase_							,					UpperCamelCase_       )





def 			lowercase(	UpperCamelCase_       )		->	List[str]:







 '''simple docstring'''
 # projection head is used in the self-supervised pre-training in MSN,
 # for downstream task it's not needed.
 UpperCamelCase			  =      [
     """module.fc.fc1.weight""",
     """module.fc.fc1.bias""",
     """module.fc.bn1.weight""",
     """module.fc.bn1.bias""",
     """module.fc.bn1.running_mean""",
     """module.fc.bn1.running_var""",
     """module.fc.bn1.num_batches_tracked""",
     """module.fc.fc2.weight""",
     """module.fc.fc2.bias""",
     """module.fc.bn2.weight""",
     """module.fc.bn2.bias""",
     """module.fc.bn2.running_mean""",
     """module.fc.bn2.running_var""",
     """module.fc.bn2.num_batches_tracked""",
     """module.fc.fc3.weight""",
     """module.fc.fc3.bias""",
 ]
 for k in ignore_keys:
  state_dict.pop(UpperCamelCase_							,					UpperCamelCase_       )





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )		->	Optional[int]:







 '''simple docstring'''
 UpperCamelCase			  =      dct.pop(UpperCamelCase_       )
 UpperCamelCase			  =      val





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_       )		->	Dict:







 '''simple docstring'''
 UpperCamelCase			  =      ViTMSNConfig()
 UpperCamelCase			  =      1000

 UpperCamelCase			  =      """datasets/huggingface/label-files"""
 UpperCamelCase			  =      """imagenet-1k-id2label.json"""
 UpperCamelCase			  =      json.load(open(hf_hub_download(UpperCamelCase_							,					UpperCamelCase_       )							,					"""r"""       )       )
 UpperCamelCase			  =      {int(UpperCamelCase_       ): v for k, v in idalabel.items()}
 UpperCamelCase			  =      idalabel
 UpperCamelCase			  =      {v: k for k, v in idalabel.items()}

 if "s16" in checkpoint_url:
  UpperCamelCase			  =      384
  UpperCamelCase			  =      1536
  UpperCamelCase			  =      6
 elif "l16" in checkpoint_url:
  UpperCamelCase			  =      1024
  UpperCamelCase			  =      4096
  UpperCamelCase			  =      24
  UpperCamelCase			  =      16
  UpperCamelCase			  =      0.1
 elif "b4" in checkpoint_url:
  UpperCamelCase			  =      4
 elif "l7" in checkpoint_url:
  UpperCamelCase			  =      7
  UpperCamelCase			  =      1024
  UpperCamelCase			  =      4096
  UpperCamelCase			  =      24
  UpperCamelCase			  =      16
  UpperCamelCase			  =      0.1

 UpperCamelCase			  =      ViTMSNModel(UpperCamelCase_       )

 UpperCamelCase			  =      torch.hub.load_state_dict_from_url(UpperCamelCase_							,					map_location="""cpu"""       )["""target_encoder"""]

 UpperCamelCase			  =      ViTImageProcessor(size=config.image_size       )

 remove_projection_head(UpperCamelCase_       )
 UpperCamelCase			  =      create_rename_keys(UpperCamelCase_							,					base_model=UpperCamelCase_       )

 for src, dest in rename_keys:
  rename_key(UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )
 read_in_q_k_v(UpperCamelCase_							,					UpperCamelCase_							,					base_model=UpperCamelCase_       )

 model.load_state_dict(UpperCamelCase_       )
 model.eval()

 UpperCamelCase			  =      """http://images.cocodataset.org/val2017/000000039769.jpg"""

 UpperCamelCase			  =      Image.open(requests.get(UpperCamelCase_							,					stream=UpperCamelCase_       ).raw       )
 UpperCamelCase			  =      ViTImageProcessor(
     size=config.image_size							,					image_mean=UpperCamelCase_							,					image_std=UpperCamelCase_       )
 UpperCamelCase			  =      image_processor(images=UpperCamelCase_							,					return_tensors="""pt"""       )

 # forward pass
 torch.manual_seed(2       )
 UpperCamelCase			  =      model(**UpperCamelCase_       )
 UpperCamelCase			  =      outputs.last_hidden_state

 # The following Colab Notebook was used to generate these outputs:
 # https://colab.research.google.com/gist/sayakpaul/3672419a04f5997827503fd84079bdd1/scratchpad.ipynb
 if "s16" in checkpoint_url:
  UpperCamelCase			  =      torch.tensor([[-1.0_9_1_5, -1.4_8_7_6, -1.1_8_0_9]]       )
 elif "b16" in checkpoint_url:
  UpperCamelCase			  =      torch.tensor([[1_4.2_8_8_9, -1_8.9_0_4_5, 1_1.7_2_8_1]]       )
 elif "l16" in checkpoint_url:
  UpperCamelCase			  =      torch.tensor([[4_1.5_0_2_8, -2_2.8_6_8_1, 4_5.6_4_7_5]]       )
 elif "b4" in checkpoint_url:
  UpperCamelCase			  =      torch.tensor([[-4.3_8_6_8, 5.2_9_3_2, -0.4_1_3_7]]       )
 else:
  UpperCamelCase			  =      torch.tensor([[-0.1_7_9_2, -0.6_4_6_5, 2.4_2_6_3]]       )

 # verify logits
 assert torch.allclose(last_hidden_state[:, 0, :3]							,					UpperCamelCase_							,					atol=1E-4       )

 print(f"""Saving model to {pytorch_dump_folder_path}"""       )
 model.save_pretrained(UpperCamelCase_       )

 print(f"""Saving image processor to {pytorch_dump_folder_path}"""       )
 image_processor.save_pretrained(UpperCamelCase_       )


if __name__ == "__main__":
     _SCREAMING_SNAKE_CASE 		=      argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         """--checkpoint_url""",
         default="""https://dl.fbaipublicfiles.com/msn/vits16_800ep.pth.tar""",
         type=str,
         help="""URL of the checkpoint you'd like to convert.""",
     )
     parser.add_argument(
         """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
     )

     _SCREAMING_SNAKE_CASE 		=      parser.parse_args()
     convert_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)

from datetime import datetime

import requests
from bsa import BeautifulSoup

if __name__ == "__main__":
     _SCREAMING_SNAKE_CASE 		=      input("""Enter image url: """).strip()
     print(F'''Downloading image from {url} ...''')
     _SCREAMING_SNAKE_CASE 		=      BeautifulSoup(requests.get(url).content, """html.parser""")
     # The image URL is in the content field of the first meta tag with property og:image
     _SCREAMING_SNAKE_CASE 		=      soup.find("""meta""", {"""property""": """og:image"""})["""content"""]
     _SCREAMING_SNAKE_CASE 		=      requests.get(image_url).content
     _SCREAMING_SNAKE_CASE 		=      F'''{datetime.now():%Y-%m-%d_%H:%M:%S}.jpg'''
     with open(file_name, """wb""") as fp:
          fp.write(image_data)
     print(F'''Done. Image saved to disk as {file_name}.''')
